High-emission diesel vehicle identification method and system based on vehicle-mounted diagnostic data

Abstract

The invention discloses a high-emission diesel vehicle identification method and system based on vehicle-mounted diagnostic data. The method comprises the following steps: acquiring basic data of a diesel vehicle; the diesel vehicle basic data comprises diesel vehicle online monitoring data collected in real time through a vehicle-mounted terminal system; adding a timestamp and a vehicle identification code to the diesel vehicle online monitoring data; on the basis of a data processing rule, the diesel vehicle online monitoring data is cleaned and filtered to ensure the accuracy of the data; according to the processed data, nitric oxide mass flow and emission factors of the diesel vehicle are calculated; the calculation process of the emission factor comprises the following steps: according to the processed data, judging whether the vehicle travel accords with a second-by-second sampling characteristic or not; if yes, the nitrogen oxide mass flow and the engine fuel flow are added up, and the ratio of the nitrogen oxide mass flow and the engine fuel flow serves as the emission factor of the day; otherwise, the emission factor is calculated second by second according to the ratio of the nitric oxide mass flow to the engine fuel flow, and the median of the data sequence is taken as the emission factor of the day; and determining the high-emission diesel vehicle according to the nitrogen oxide mass flow and the emission factor. According to the method, the emission factors are introduced, and the emission factors and the nitrogen oxide emission amount are simultaneously used as judgment criteria to evaluate the emission performance of the vehicle, so that the accuracy and effectiveness of diesel vehicle emission supervision can be improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental monitoring and supervision technology, and in particular to a method, system, terminal device and computer-readable storage medium for identifying high-emission diesel vehicles based on on-board diagnostic data. Background Art

[0002] With the continuous increase in the number of motor vehicles, exhaust emissions from diesel vehicles, especially nitrogen oxides (NOx), have become one of the main sources of air pollution. At present, NOx emissions from diesel vehicles account for the vast majority of total NOx emissions from motor vehicles, which has a serious impact on air quality. In order to meet this challenge, environmental protection departments have gradually strengthened supervision of high-emission diesel vehicles, especially vehicles that exceed emission standards during actual driving. However, existing technical means mainly rely on regular emission detection, which makes it difficult to identify high-emission vehicles in real time and accurately during vehicle driving.

[0003] The emission behavior of diesel vehicles is affected by many factors, including the vehicle's operating conditions, driving habits, maintenance status, etc. Therefore, relying solely on emission test results is difficult to reflect the vehicle's emission performance on the actual road. In recent years, diesel vehicle OBD (on-board diagnostic system) has been widely used to monitor the vehicle's operating conditions and emission indicators in real time. However, there is currently a lack of a standardized means to accurately identify high-emission diesel vehicles by analyzing the multi-dimensional monitoring data collected by the OBD system. This technical gap leads to the risk of misjudgment and missed judgment in the identification process of high-emission vehicles, affecting the effectiveness of supervision. Summary of the invention

[0004] In order to address the deficiencies of the above-mentioned prior art, the present invention provides a method, system, terminal device and computer-readable storage medium for identifying high-emission diesel vehicles based on on-board diagnostic data. By introducing emission factors and using them together with nitrogen oxide emissions as evaluation criteria to evaluate the emission performance of vehicles, the accuracy and effectiveness of diesel vehicle emission regulation can be improved.

[0005] The first object of the present invention is to provide a method for identifying high-emission diesel vehicles based on on-board diagnostic data.

[0006] A second object of the present invention is to provide a high emission diesel vehicle identification system based on on-board diagnostic data.

[0007] The third object of the present invention is to provide a terminal device.

[0008] A fourth object of the present invention is to provide a computer-readable storage medium.

[0009] The first object of the present invention can be achieved by adopting the following technical solutions:

[0010] A method for identifying high-emission diesel vehicles based on on-board diagnostic data, the method comprising:

[0011] Obtaining basic data of diesel vehicles; the basic data of diesel vehicles includes online monitoring data of diesel vehicles collected in real time by a vehicle terminal system; the online monitoring data of diesel vehicles is supplemented with a timestamp and a vehicle identification code;

[0012] Based on the data processing rules, the online monitoring data of the diesel vehicle is cleaned and filtered to ensure the accuracy of the data;

[0013] Calculate the nitrogen oxide mass flow rate and emission factor of the diesel vehicle based on the processed data; the calculation process of the emission factor includes: judging whether the vehicle trip meets the second-by-second sampling characteristics based on the processed data; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day;

[0014] Identify high emission diesel vehicles based on NOx mass flow and emission factors.

[0015] Furthermore, if the second-by-second sampling feature is met, the formula for calculating the emission factor is:

[0016]

[0017] Among them, E 1 is the emission factor for the day, Q i 、VFUEL (i) are the nitrogen oxide mass flow rate and engine fuel flow rate at the ith collection moment of a diesel vehicle during normal driving, respectively, and n is the number of collection times.

[0018] Furthermore, if the second-by-second sampling feature is not met, the formula for calculating the emission factor is:

[0019]

[0020] Among them, E 2 is the emission factor for the day, MEDIAN() is a function that calculates the median of a set of values, Q i 、VFUEL (i) are the nitrogen oxide mass flow rate and engine fuel flow rate at the ith collection moment of a diesel vehicle during normal driving, respectively, and n is the number of collection times.

[0021] Furthermore, for normal driving, diesel vehicles should meet VSPEED (i) ≤θ (Speedlimit) And VFUEL(i) <θ (Fuellimit) Conditions; Among them, VSPEED (i) and VFUEL (i) are the speed and engine fuel flow of the diesel vehicle at the i-th collection time, θ (Speedlimit) and θ (Fuellimit) These are all set thresholds.

[0022] Furthermore, judging whether the vehicle travel meets the second-by-second sampling characteristics based on the processed data includes:

[0023] According to the processed data, calculate the time interval Δt of the data of consecutive adjacent time points before and after i , all calculated values ​​are taken as a dataset of adjacent time points ΔT(Δt 1 ,Δt 2 ,...,Δt n-1 ), when the elements of the data set satisfy Δt i >θ (tr) , it means that the vehicle's previous trip has ended and a new trip has started; where θ (tr) is the threshold of the time interval between adjacent time points, and the trip data set TR (tr 1 ,tr 2 ,...,tr m ), i is the i-th collection time, n is the number of collections, 1≤m <n;

[0024] Calculate the travel time tr i The interval between any two adjacent time points is t i-1 ,t i The time difference between the two time points is obtained to obtain the interval data set of adjacent time points within the trip ΔT(tr i )(Δt 1 ,Δt 2 ,...,Δt p ), when ΔT(tr i ) The elements of the data set satisfy Δt p <θ (contr) When , it is determined that the vehicle trip meets the second-by-second sampling characteristics; where 2≤i <m,1≤p<m,θ (contr) The threshold for the time interval that meets the second-by-second sampling characteristics.

[0025] Furthermore, the data processing rules include:

[0026] Trip: Based on the online monitoring data of diesel vehicles, calculate the time interval Δt between the consecutive adjacent time points i , all calculated values ​​are taken as a dataset of adjacent time points ΔT(Δt 1 ,Δt 2,...,Δt n-1 ), when the elements of the data set satisfy Δt i >θ (tr) , it means that the vehicle's previous trip has ended and a new trip has started; where θ (tr) is the threshold of the time interval between adjacent time points, and the trip data set TR (tr 1 ,tr 2 ,...,tr m ), i is the i-th collection time, n is the number of collections, 1≤m <n;

[0027] Invalid trip: For each subtrip tr of the trip data set i , use the end time of the trip minus the start time of the trip to get the trip time interval Δtrt i , all calculated values ​​are taken as a travel time interval data set ΔTRT(Δtrt 1 ,Δtrt 2 ,...,Δtrt m ), when the elements of the data set satisfy Δtrt i <θ (invtr) , it means that the sub-trip is an invalid trip; where θ (invtr) is the threshold of the time interval of invalid trips, and the invalid trip data set INVTR (tr 1 ,tr 2 ,...,tr k ), 0≤k≤m;

[0028] Sampling travel time per second: Calculating travel time tr i The interval between any two adjacent time points is t i-1 ,t i The time difference between the two time points is obtained to obtain the interval data set of adjacent time points within the trip ΔT(tr i )(Δt 1 ,Δt 2 ,...,Δt p ), when ΔT(tr i ) The elements of the data set satisfy Δt p <θ (contr) When , it means that the vehicle trip meets the second-by-second sampling characteristics; where θ (contr) is the threshold of the time interval that meets the second-by-second sampling characteristics, and thus the second-by-second sampling trip data set CONTR (tr 1 ,tr 2 ,...,tr j ), 0≤j≤m;

[0029] Monthly travel days: Define the monthly travel days of the vehicle as D (m), m is the month; the vehicle’s trip data set TR (tr 1 ,tr 2 ,...,tr m ) minus the invalid trip data set INVTR(tr 1 ,tr 2 ,...,tr k ), the remaining is the number of valid sub-trips; when LEN(TR-INVTR)≥1, it means that the vehicle has a travel record on that day, D (m) =D (m) +1.

[0030] Furthermore, the diesel vehicle online monitoring data includes the concentration of nitrogen oxide emissions, the engine air intake volume and the engine fuel flow rate;

[0031] The cleaning and filtering of the diesel vehicle online monitoring data includes:

[0032] Delete invalid trip data sets;

[0033] Delete records with abnormal nitrogen oxide emission concentration values;

[0034] Delete records with abnormal dispersion of nitrogen oxide emission concentration values;

[0035] Delete records with abnormal differences in nitrogen oxide emission concentration values;

[0036] Delete the records with abnormal discreteness of intake volume values;

[0037] Delete the records with abnormal intake volume value difference;

[0038] Delete records with abnormal intake volume values;

[0039] Delete records of abnormal engine fuel flow values;

[0040] Delete the number of vehicle monthly travel days D (m) Vehicles and their monitoring information that do not meet the required minimum number of days are included to reduce numerical errors that may be caused by insufficient sampling.

[0041] Furthermore, the determination of high emission diesel vehicles according to the nitrogen oxide mass flow rate and emission factor includes:

[0042] Calculate the nitrogen oxide emissions for the day based on the nitrogen oxide mass flow rate;

[0043] Based on the nitrogen oxide emissions and emission factors, a quadrant diagram is drawn and high-emission vehicles are determined: when the nitrogen oxide emissions and emission factors in the first quadrant exceed the respectively set thresholds, the corresponding vehicle is determined to be a high-emission diesel vehicle.

[0044] The second object of the present invention can be achieved by adopting the following technical solutions:

[0045] A high emission diesel vehicle identification system based on on-board diagnostic data, the system comprising:

[0046] A data acquisition module is used to acquire basic data of diesel vehicles; the basic data of diesel vehicles includes online monitoring data of diesel vehicles collected in real time by the vehicle terminal system; the online monitoring data of diesel vehicles is supplemented with a timestamp and a vehicle identification code;

[0047] A data processing module, used for cleaning and filtering the diesel vehicle online monitoring data based on data processing rules to ensure the accuracy of the data;

[0048] A calculation module is used to calculate the nitrogen oxide mass flow rate and emission factor of the diesel vehicle according to the processed data; the calculation process of the emission factor includes: judging whether the vehicle travel meets the second-by-second sampling characteristics according to the processed data; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day;

[0049] Identification module for determining high-emitting diesel vehicles based on NOx mass flow and emission factors.

[0050] The third object of the present invention can be achieved by adopting the following technical solutions:

[0051] A terminal device comprises a processor and a memory for storing a program executable by the processor. When the processor executes the program stored in the memory, the above-mentioned high-emission diesel vehicle identification method based on on-board diagnostic data is implemented.

[0052] The fourth object of the present invention can be achieved by adopting the following technical solutions:

[0053] A computer-readable storage medium stores a program, which, when executed by a processor, implements the above-mentioned high-emission diesel vehicle identification method based on on-board diagnostic data.

[0054] The present invention has the following beneficial effects compared with the prior art:

[0055] The present invention overcomes the limitation of the prior art that only relies on excessive nitrogen oxide emissions as the only judgment criterion; by combining the diesel vehicle OBD real-time monitoring data and emission factors, it not only makes judgments based on the NOx emissions exceeding the legal limit, but also considers various factors affecting emissions such as vehicle operating conditions and driving behavior, so as to more accurately evaluate the vehicle's emission performance; by introducing emission factors, the accuracy of identifying high-emission diesel vehicles is greatly improved, and the problem of misjudgment or missed judgment caused by a single standard is reduced, providing more scientific and comprehensive technical support for environmental protection supervision, and effectively helping to reduce atmospheric pollutant emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0057] Figure 1 This is a flow chart of a method for identifying high-emission diesel vehicles based on on-board diagnostic data according to an embodiment of the present invention;

[0058] Figure 2 A flowchart of calculating emission factors according to an embodiment of the present invention;

[0059] Figure 3 A schematic diagram of a high-emission diesel vehicle identification quadrant diagram according to an embodiment of the present invention;

[0060] Figure 4 This is a structural block diagram of a high-emission diesel vehicle identification system based on on-board diagnostic data according to an embodiment of the present invention;

[0061] Figure 5 It is a structural block diagram of a terminal device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be understood that the specific embodiments described are only used to explain the present application and are not used to limit the present application.

[0063] Example:

[0064] The high-emission diesel vehicle identification method based on on-board diagnostic data provided in this embodiment is implemented on the server side, and all the real-time data of the OBD online monitoring of heavy-duty diesel vehicles are first uniformly gathered to the server side. The server side uses this method to calculate the emission factor of the diesel vehicle, so as to determine whether it is a high-emission diesel vehicle. In order to facilitate the operation of users and administrators, a software platform can be tailored to achieve convenient interactive control. The server side can be flexibly configured according to actual needs. It can be an independent single server or an efficient and collaborative server group, which is applicable to both centralized architecture and distributed deployment. In addition, in order to improve data processing efficiency and security, it is also possible to consider setting up a dedicated storage end to centrally store the massive real-time data of the OBD online monitoring of heavy-duty diesel vehicles. The storage end also supports diversified configurations, ranging from a single storage device to a high-performance storage group. The server side can efficiently retrieve data from these storage resources, and execute the above method to analyze the data to ensure the smoothness and efficiency of the entire process.

[0065] like Figure 1 As shown, the high emission diesel vehicle identification method based on vehicle diagnostic data of this embodiment includes the following steps:

[0066] S110. Obtain basic diesel vehicle data.

[0067] The basic data of diesel vehicles include basic vehicle information and online monitoring data.

[0068] Specifically, the basic vehicle information includes vehicle emission stage, vehicle brand, vehicle manufacturer, vehicle model, engine model, vehicle category, vehicle identification code and other information.

[0069] Specifically, the online monitoring data includes receiving and storing vehicle real-time monitoring data and OBD device abnormal information from the OBD interface of heavy-duty diesel vehicles for subsequent data analysis, emission control and maintenance management.

[0070] The vehicle real-time monitoring data is communicated with the diesel vehicle's ECU (electronic control unit) through the standard OBD interface, and multiple data items of the vehicle are collected in real time at a speed of 1HZ according to national standards, including but not limited to:

[0071] Emission data: Real-time value of vehicle nitrogen oxide (NOx) emissions. The system extracts NOx concentration data from relevant sensors in the OBD in ppm, and the data is updated every second to ensure that vehicle emissions meet national standards.

[0072] Intake volume: Records the intake flow of the engine. The data is provided in real time by the OBD sensor in kg / h. The intake volume data can be used to evaluate the combustion efficiency and the effectiveness of the emission control strategy.

[0073] Engine fuel flow: Fuel flow data is obtained through the relevant sensors in the OBD interface, and the unit is L / h. This data item can accurately record the fuel consumption of diesel vehicles under different loads and driving conditions.

[0074] Other important data: The system also collects other important vehicle operating parameters, such as vehicle location information (latitude and longitude coordinates), vehicle speed, engine speed, etc. All data are transmitted and recorded in real time through the OBD communication protocol.

[0075] OBD device abnormality information is to monitor the abnormal status of OBD devices, record and classify adverse conditions that may affect vehicle performance or emissions, including:

[0076] Abnormal events: When the system detects an abnormality, such as NOx emission values ​​exceeding the standard, abnormal reduction in intake volume, engine fault code (DTC) triggering, etc., the system will immediately record the abnormal event and generate a corresponding timestamp and error code.

[0077] Classification and priority processing of abnormal events: The system classifies the abnormal events received, such as serious emission-related abnormalities (such as serious NOx exceeding the standard) and mechanical failure-related abnormalities (such as engine overheating), and assigns different priorities according to the severity, so as to optimize maintenance and repair resources in subsequent processing.

[0078] All collected real-time monitoring data and equipment abnormality information are stored in the database. The following information is added when the data is stored:

[0079] Timestamp: All data items are marked with the acquisition time to ensure time consistency in subsequent analysis.

[0080] Vehicle identification information: Each data entry is associated with a unique vehicle identifier (such as a VIN number) so that it can be accurately mapped to a specific vehicle when data is retrieved.

[0081] S120: Processing basic data of diesel vehicles.

[0082] Define data processing rules, clean and filter the collected diesel vehicle OBD online monitoring data, correct or remove erroneous, abnormal, incomplete or duplicate information to ensure data accuracy.

[0083] Specifically, before cleaning and filtering the data, the OBD monitoring data information definition needs to be clarified, mainly including the following aspects:

[0084] Idle state: When some values ​​in the vehicle real-time monitoring data meet VSPEED (i) ≤θ (Speedlimit) or VFUEL (i) <θ (Fuellimit), the vehicle is considered to be in idling state at the moment of data collection; VSPEED (i) It represents the speed value of a vehicle in the i-th real-time monitoring data. The speed does not exceed θ (Speedlimit) When the limit is reached, the vehicle is considered to be in idle state; VFUEL (i) It represents the value of the engine fuel flow rate in the real-time monitoring data of a vehicle for the i-th time. The engine fuel flow rate does not exceed θ (Fuellimit) When the limit is reached, the vehicle is considered to be in idle state.

[0085] Trip: Based on the real-time monitoring data of the vehicle, calculate the time interval Δt between the data of consecutive adjacent time points i , all calculated values ​​are taken as a dataset of adjacent time points ΔT(Δt 1 ,Δt 2 ,...,Δt n-1 ), when the elements of the data set satisfy Δt i >θ (tr) , it is considered that the vehicle's previous trip has ended and a new trip has started; where θ (tr) represents the threshold of the time interval between adjacent time points, and thus the trip data set TR (tr 1 ,tr 2 ,...,tr m ), 1≤m <n;

[0086] Invalid trip: Calculate trip tr i The time span is Δtrt, that is, for each sub-trip of the trip data set, the end time of the trip is subtracted from the start time of the trip to obtain the trip time interval Δtrt. i , all calculated values ​​are taken as a travel time interval data set ΔTRT(Δtrt 1 ,Δtrt 2 ,...,Δtrt m ), when the elements of the data set satisfy Δtrt i <θ (invtr) When , the sub-trip is considered to be an invalid trip; where θ (invtr) The threshold of the time interval of the invalid trip is used to obtain the invalid trip data set INVTR (tr 1 ,tr 2 ,...,tr k ), 0≤k≤m;

[0087] Sampling travel time per second: Calculating travel time tr i The interval between any two adjacent time points is t i-1 ,t i The time difference between the two time points is obtained to obtain the interval data set of adjacent time points within the trip ΔT(tr i )(Δt1 ,Δt 2 ,...,Δt p ), when ΔT(tr i ) The elements of the data set satisfy Δt p <θ (contr) When , the vehicle trip is considered to meet the second-by-second sampling characteristics; where θ (contr) The threshold value of the time interval that meets the second-by-second sampling characteristics is obtained, and the second-by-second sampling trip data set CONTR (tr 1 ,tr 2 ,...,tr j ), 0≤j≤m; 0≤j≤m;

[0088] Monthly travel days: define the monthly travel days of the vehicle as D (m) , where m is the month; the vehicle’s trip data set TR (tr 1 ,tr 2 ,...,tr m ) minus the invalid trip data set INVTR(tr 1 ,tr 2 ,...,tr k ), when the number of remaining valid sub-trips, i.e. LEN(TR-INVTR) is greater than or equal to 1, it is considered that the vehicle has a travel record on that day, and the travel record is increased by 1, i.e. D (m) =D (m) +1.

[0089] Specifically, filtering and cleaning the OBD monitoring data information of heavy-duty diesel vehicles mainly includes the following steps:

[0090] Delete invalid trip data set INVTR(tr 1 ,tr 2 ,...,tr k );

[0091] Delete the records with abnormal nitrogen oxide value limits in the vehicle real-time monitoring data, that is, the nitrogen oxide value also meets the conditions of VNOx (i) ≤θ (NOxlower) And VNOx (i) >θ (NOxupper) If there is no record, it is considered that the nitrogen oxide numerical limit is abnormal; among them, VNOx (i) represents the nitrogen oxide value in the real-time monitoring data of a certain vehicle, θ (NOxlower) Indicates the lower limit of the nitrogen oxide value, θ (NOxupper) Indicates the upper limit of the nitrogen oxide value; the nitrogen oxide value must meet (θ (NOxlower) ,θ (NOxupper) ], otherwise it is considered that the nitrogen oxide numerical limit in the vehicle real-time monitoring data is abnormal;

[0092] Delete the records with abnormal dispersion of nitrogen oxide values ​​in the real-time monitoring data of vehicles, that is, the nitrogen oxide values ​​satisfy σ(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) ))<θ stdnox and LEN(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) ))<θ (Rlimit) If the record is not found, it is considered that the dispersion of nitrogen oxide values ​​is abnormal. (1) ,VNOx (2) ,…,VNOx (n) ) represents the set of nitrogen oxide values ​​in the real-time monitoring data of a vehicle, σ(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) )) represents the standard deviation of nitrogen oxide data, θ stdnox represents the threshold value that needs to be met when calculating the oxide standard deviation; LEN(R(VNOx(1), VNOx(2), …, VNOx(n))) represents the length of the nitrogen oxide data, θ (Rlimit) Indicates the threshold value of the vehicle real-time monitoring data volume;

[0093] Delete the records with abnormal NOx value difference in vehicle real-time monitoring data, that is, the NOx value meets MAX(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) ))=MIN(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) )) records, the difference in the nitrogen oxide values ​​is considered abnormal; among them, MAX(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) )) represents the maximum value of nitrogen oxide data, MIN(R(VNOx (1) ,VNOx (2) ,…,VNOx (n) )) represents the minimum value of nitrogen oxide data.

[0094] Delete the records with abnormal discreteness of intake air volume values ​​in the real-time monitoring data of the vehicle, that is, the intake air volume values ​​satisfy σ(R(VAIR (1) ,VAIR (2) …VAIR (n)))=0, it is considered that the intake air volume dispersion is abnormal; (1) ,VAIR (2) …VAIR (n) ) represents the intake volume value set in the real-time monitoring data of a vehicle, σ(R(VAIR (1) ,VAIR (2) …VAIR (n) )) represents the standard deviation of the intake volume data;

[0095] Delete the records with abnormal intake volume value difference in the vehicle real-time monitoring data, that is, the intake volume value meets MAX(R(VAIR (1) ,VAIR (2) …VAIR (n) ))=MIN(R(VAIR (1) ,VAIR (2) …VAIR (n) )) records, it is considered that the intake volume value difference is abnormal; among them, MAX(R(VAIR (1) ,VAIR (2) …VAIR (n) )) indicates the maximum value of the intake volume data, MIN(R(VAIR (1) ,VAIR (2) …VAIR (n) )) represents the minimum value of the intake volume data;

[0096] Delete the records with abnormal engine fuel flow values ​​in the vehicle real-time monitoring data, that is, the engine fuel flow values ​​also meet VFUEL (i) ≤θ (Fuellower) And VFUEL (i) >θ (Fuelupper) If the record of the engine fuel flow is abnormal, the value of the engine fuel flow is considered abnormal; (Fuellower) Indicates the lower limit threshold of the engine fuel flow rate, θ (Fuelupper) Indicates the upper limit threshold of the engine fuel flow value; the engine fuel flow value must meet (θ (Fuellower) ,θ (Fuelupper) )], otherwise it is considered that the engine fuel flow value in the vehicle real-time monitoring data is abnormal;

[0097] Delete the records with abnormal intake volume values ​​in the vehicle real-time monitoring data, that is, the intake volume values ​​meet VAIR at the same time (i) ≤θ (Airlower) And VAIR (i) >θ (Airupper) If there is a record of the intake volume, it is considered that the intake volume value is abnormal; among them, VAIR (i) Indicates the intake volume value of a vehicle in a certain real-time monitoring data, θ (Airlower)Indicates the lower limit threshold of the intake volume, θ (Airupper) Indicates the upper limit of the intake volume value; the intake volume value must meet (θ (Airlower) ,θ (Airupper) )], otherwise the intake air volume value in the vehicle real-time monitoring data is considered abnormal;

[0098] Delete the number of vehicle monthly travel days D (m) Vehicles and their monitoring information that do not meet the minimum number of days required are used to reduce the numerical error that may be caused by insufficient sampling, that is, D (m) <θ (Daylimit) ; where θ (Daylimit) is the threshold for the minimum number of days required.

[0099] S130. Calculate the nitrogen oxide mass flow rate and emission factor of the diesel vehicle based on the processed data.

[0100] In an embodiment, the processor on the server side can obtain the emission factor of the diesel vehicle by calculation.

[0101] Furthermore, if Figure 2 As shown, step S130 specifically includes the following steps:

[0102] The first step is to calculate the nitrogen oxide mass flow rate of diesel vehicles.

[0103] Specifically, the nitrogen oxide mass flow rate of diesel vehicles is calculated based on the following formula:

[0104]

[0105] Among them, Q i represents the instantaneous nitrogen oxide mass flow rate of a diesel vehicle during normal driving; ρ o Indicates the average density of diesel. The density range of national standard diesel is 0.83~0.855g / ml; δ air Indicates the average molecular weight of air, usually set to δ air =28.8 g / mol; v nox Indicates the average molecular weight of nitrogen oxides, generally set to v nox =46 g / mol;

[0106] The second step is to determine whether the OBD data is sampled second by second.

[0107] According to the second-by-second sampling trip data set CONTR(tr 1 ,tr 2 ,...,tr j ) to determine whether the vehicle meets the second-by-second sampling feature; if the vehicle OBD data meets the "second-by-second sampling" feature, method 1 is used to calculate the diesel vehicle emission factor, otherwise method 2 is used to calculate the diesel vehicle emission factor.

[0108] Method 1: Sum up the NOx mass flow and engine fuel flow respectively, and use the ratio of the two as the emission factor for the day.

[0109] Specifically, the diesel vehicle emission factor E is calculated based on the following formula: 1 :

[0110]

[0111] Method 2: Calculate the emission factor second by second and take the median of the data series as the emission factor for the day.

[0112] Specifically, the diesel vehicle emission factor E is calculated based on the following formula: 2 :

[0113]

[0114] S140. Determine high emission diesel vehicles based on nitrogen oxide mass flow rate and emission factor.

[0115] A quadrant diagram is drawn based on the calculated nitrogen oxide emissions and emission factors. When both the nitrogen oxide emissions and the emission factors exceed the threshold at the same time, the vehicle is identified as a high-emission diesel vehicle.

[0116] The nitrogen oxides emissions for the day were obtained by calculating the average nitrogen oxides mass flow rate of each diesel vehicle based on the following formula:

[0117] G nox =AVG(VNOx (1) ,VNOx (2) ,...,VNOx (n) ).

[0118] The nitrogen oxide emissions are combined with the emission factors to obtain the emission quadrant dataset for all vehicles (E (i)(j) ,G nox(j) ), where i∈(1,2), j∈(1,2,...,n), i represents the emission factor calculation method used by the diesel vehicle, and j represents the diesel vehicle to be calculated.

[0119] Reference Figure 3 , when the emission quadrant dataset (E (i)(j) ,G nox(j) ) in which the sub-elements satisfy E (i)(j) >θ (E) And G nox(j) >θ (G) When , the vehicle is considered to be a high-emission diesel vehicle; where θ (E) represents the threshold of the emission factor, θ (G)Indicates the threshold of nitrogen oxides. Refer to the relevant national requirements for the emission limits of nitrogen oxides for diesel vehicles. You can set θ (G) =1500ppm.

[0120] This embodiment introduces the diesel vehicle emission factor as an important factor. While the nitrogen oxide emissions of diesel vehicles exceed the threshold as the cornerstone of judgment, it also combines the emission factor as an important indicator. The introduction of this dual standard greatly enhances the accuracy and reliability of identifying high-emission diesel vehicles. Compared with the existing technology that only relies on the determination of nitrogen oxide emissions, the method provided by this embodiment effectively reduces the risk of misjudgment that may be caused by the standardization of the single standard, and realizes more accurate and comprehensive identification and supervision of high-emission diesel vehicles.

[0121] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be completed by instructing related hardware through a program, and the corresponding program may be stored in a computer-readable storage medium.

[0122] It should be noted that although the method operations of the above embodiments are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired results. On the contrary, the steps depicted can be performed in a different order. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, and / or one step can be decomposed into multiple steps.

[0123] like Figure 4 As shown, this embodiment also provides a high-emission diesel vehicle identification system based on vehicle diagnostic data, including a data acquisition module 410, a data processing module 420, a calculation module 430 and an identification module 440, wherein:

[0124] The data acquisition module 410 is used to acquire basic data of diesel vehicles; the basic data of diesel vehicles includes online monitoring data of diesel vehicles collected in real time by the vehicle terminal system; the online monitoring data of diesel vehicles is supplemented with a timestamp and a vehicle identification code;

[0125] The data processing module 420 is used to clean and filter the diesel vehicle online monitoring data based on data processing rules to ensure the accuracy of the data;

[0126] The calculation module 430 is used to calculate the nitrogen oxide mass flow rate and emission factor of the diesel vehicle according to the processed data; the calculation process of the emission factor includes: judging whether the vehicle travel meets the second-by-second sampling characteristics according to the processed data; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day;

[0127] The identification module 440 is used to determine the high emission diesel vehicle according to the nitrogen oxide mass flow rate and the emission factor.

[0128] The specific implementation of each module in the embodiment can refer to the above method, which will not be described one by one here; it should be noted that the system provided in this embodiment is only illustrated by the division of the above-mentioned functional modules. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure can be divided into different functional modules to complete all or part of the functions described above.

[0129] The modules in the embodiments may include hardware, a combination of hardware and software, or a computer-readable medium containing executable instructions, which, when executed by a processor, can implement the various functional characteristics described in the present invention. It is worth noting that the scope of application of the present invention is not limited to the specific hardware and / or software features contained in the modules specifically mentioned in the embodiments, unless there is a clear indication requirement. As an example and not limitation, one or more embodiments of the present invention can be implemented by one or more processors executing software instructions.

[0130] This embodiment provides a terminal device, which can be a computer. Figure 5As shown, a processor 520, a memory, an input device 530, a display 540 and a network interface 550 are connected through a system bus 510. The processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium 560 and an internal memory 570. The non-volatile storage medium 560 stores an operating system, a computer program and a database. The internal memory 570 provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. When the processor 520 executes the computer program stored in the memory, the high-emission diesel vehicle identification method based on on-board diagnostic data of the above-mentioned embodiment 1 is implemented, that is, obtaining basic vehicle data of the diesel vehicle; the basic vehicle data of the diesel vehicle includes online monitoring data of the diesel vehicle collected in real time by the on-board terminal system; control data; the diesel vehicle online monitoring data is attached with a timestamp and a vehicle identification code; based on data processing rules, the diesel vehicle online monitoring data is cleaned and filtered to ensure the accuracy of the data; based on the processed data, the nitrogen oxide mass flow rate and emission factor of the diesel vehicle are calculated; the calculation process of the emission factor includes: based on the processed data, judging whether the vehicle journey meets the second-by-second sampling characteristics; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day; determining the high-emission diesel vehicle based on the nitrogen oxide mass flow rate and the emission factor.

[0131] The present embodiment provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the high-emission diesel vehicle identification method based on vehicle diagnostic data of the above-mentioned embodiment 1, namely: obtaining basic vehicle data of the diesel vehicle; the basic vehicle data of the diesel vehicle includes online monitoring data of the diesel vehicle collected in real time by the vehicle terminal system; the online monitoring data of the diesel vehicle is attached with a timestamp and a vehicle identification code; based on data processing rules, the online monitoring data of the diesel vehicle is cleaned and filtered to ensure the accuracy of the data; based on the processed data, the nitrogen oxide mass flow rate and emission factor of the diesel vehicle are calculated; the calculation process of the emission factor includes: based on the processed data, judging whether the vehicle trip meets the second-by-second sampling characteristics; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day; determining the high-emission diesel vehicle based on the nitrogen oxide mass flow rate and the emission factor.

[0132] It should be noted that the computer-readable storage medium of the present embodiment may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0133] The above is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and inventive concept of the present invention within the scope disclosed by the present invention, which shall fall within the protection scope of the present invention.

Claims

1. A method for identifying high-emission diesel vehicles based on on-board diagnostic data, characterized in that: The method comprises: Obtaining basic data of diesel vehicles; the basic data of diesel vehicles includes online monitoring data of diesel vehicles collected in real time by a vehicle terminal system; the online monitoring data of diesel vehicles is supplemented with a timestamp and a vehicle identification code; Based on the data processing rules, the online monitoring data of the diesel vehicle is cleaned and filtered to ensure the accuracy of the data; Calculate the nitrogen oxide mass flow rate and emission factor of the diesel vehicle based on the processed data; the calculation process of the emission factor includes: judging whether the vehicle trip meets the second-by-second sampling characteristics based on the processed data; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day; Identify high emission diesel vehicles based on NOx mass flow and emission factors.

2. The high emission diesel vehicle identification method according to claim 1, characterized in that: If the second-by-second sampling characteristics are met, the formula for calculating the emission factor is: Among them, E1 is the emission factor of the day, Q i 、VFUEL (i) are the nitrogen oxide mass flow rate and engine fuel flow rate at the ith collection moment of a diesel vehicle during normal driving, respectively, and n is the number of collection times.

3. The high emission diesel vehicle identification method according to claim 1, characterized in that: If the second-by-second sampling feature is not met, the formula for calculating the emission factor is: Among them, E2 is the emission factor of the day, MEDIAN() is a function to calculate the median of a set of values, Q i 、VFUEL (i) are the nitrogen oxide mass flow rate and engine fuel flow rate at the ith collection moment of a diesel vehicle during normal driving, respectively, and n is the number of collection times.

4. The high emission diesel vehicle identification method according to any one of claims 2 and 3, characterized in that: For normal driving, diesel vehicles should meet VSPEED (i) ≤θ (Speedlimit) And VFUEL (i) <θ (Fuellimit) conditions; Among them, VSPEED (i) and VFUEL (i) are the speed and engine fuel flow of the diesel vehicle at the i-th collection time, θ (Speedlimit) and θ (Fuellimit) These are all set thresholds.

5. The high emission diesel vehicle identification method according to any one of claims 1 to 3, characterized in that: The step of judging whether the vehicle travel meets the second-by-second sampling characteristics based on the processed data includes: According to the processed data, calculate the time interval Δt of the data of consecutive adjacent time points before and after i , all calculated values ​​are taken as a dataset of adjacent time points ΔT (Δt1, Δt2, ..., Δt n-1 ), when the elements of the data set satisfy Δt i >θ (tr) , it means that the vehicle's previous trip has ended and a new trip has started; where θ (tr) is the threshold of the time interval between adjacent time points, and the trip data set TR (tr1, tr2, ..., tr m ), i is the i-th collection time, n is the number of collections, 1≤m <n; Calculate the travel time tr i The interval between any two adjacent time points is t i-1 ,t i The time difference between the two time points is obtained to obtain the interval data set of adjacent time points within the trip ΔT(tr i )(Δt1,Δt2,...,Δt p ), when ΔT(tr i ) The elements of the data set satisfy Δt p <θ (contr) When , it is determined that the vehicle trip meets the second-by-second sampling characteristics; where 2≤i <m,1≤p<m,θ (contr) The threshold for the time interval that meets the second-by-second sampling characteristics.

6. The high emission diesel vehicle identification method according to claim 1, characterized in that: The data processing rules include: Trip: Based on the online monitoring data of diesel vehicles, calculate the time interval Δt between the consecutive adjacent time points i , all calculated values ​​are taken as a dataset of adjacent time points ΔT (Δt1, Δt2, ..., Δt n-1 ), when the elements of the data set satisfy Δt i >θ (tr) , it means that the vehicle's previous trip has ended and a new trip has started; where θ (tr) is the threshold of the time interval between adjacent time points, and the trip data set TR (tr1, tr2, ..., tr m ), i is the i-th collection time, n is the number of collections, 1≤m <n; Invalid trip: For each subtrip tr of the trip data set i , use the end time of the trip minus the start time of the trip to get the trip time interval Δtrt i , all calculated values ​​are taken as a travel time interval data set ΔTRT (Δtrt1, Δtrt2, ..., Δtrt m ), when the elements of the data set satisfy Δtrt i <θ (invtr) , it means that the sub-trip is an invalid trip; where θ (invtr) is the threshold of the time interval of invalid trips, and thus the invalid trip data set INVTR (tr1, tr2, ..., tr k ), 0≤k≤m; Sampling travel time per second: Calculating travel time tr i The interval between any two adjacent time points is t i-1 ,t i The time difference between the two time points is obtained to obtain the interval data set of adjacent time points within the trip ΔT(tr i )(Δt1,Δt2,...,Δt p ), when ΔT(tr i ) The elements of the data set satisfy Δt p <θ (contr) When , it means that the vehicle trip meets the second-by-second sampling characteristics; where θ (contr) is the threshold of the time interval that meets the second-by-second sampling characteristics, and thus the second-by-second sampling trip data set CONTR (tr1, tr2, ..., tr j ), 0≤j≤m; Monthly travel days: define the monthly travel days of the vehicle as D (m) , m is the month; the vehicle’s trip data set TR (tr1, tr2, ..., tr m ) minus the invalid trip data set INVTR (tr1, tr2, ..., tr k ), the remaining is the number of valid sub-trips; when LEN(TR-INVTR)≥1, it means that the vehicle has a travel record on that day, D (m) =D (m) +1.

7. The high emission diesel vehicle identification method according to claim 6, characterized in that: The diesel vehicle online monitoring data includes the concentration of nitrogen oxide emissions, the engine air intake volume and the engine fuel flow rate; The cleaning and filtering of the diesel vehicle online monitoring data includes: Delete invalid trip data sets; Delete records with abnormal nitrogen oxide emission concentration values; Delete records with abnormal dispersion of nitrogen oxide emission concentration values; Delete records with abnormal differences in nitrogen oxide emission concentration values; Delete the records with abnormal discreteness of intake volume values; Delete the records with abnormal intake volume value difference; Delete records with abnormal intake volume values; Delete records of abnormal engine fuel flow values; Delete the number of vehicle monthly travel days D (m) Vehicles and their monitoring information that do not meet the required minimum number of days are included to reduce numerical errors that may be caused by insufficient sampling.

8. The high emission diesel vehicle identification method according to any one of claims 1 to 3, 6 to 7, characterized in that: The determination of high emission diesel vehicles according to the nitrogen oxide mass flow rate and emission factor includes: Calculate the nitrogen oxide emissions for the day based on the nitrogen oxide mass flow rate; Based on the nitrogen oxide emissions and emission factors, a quadrant diagram is drawn and high-emission vehicles are determined: when the nitrogen oxide emissions and emission factors in the first quadrant exceed the respectively set thresholds, the corresponding vehicle is determined to be a high-emission diesel vehicle.

9. A high emission diesel vehicle identification system based on on-board diagnostic data, characterized in that: The system comprises: A data acquisition module is used to acquire basic data of diesel vehicles; the basic data of diesel vehicles includes online monitoring data of diesel vehicles collected in real time by the vehicle terminal system; the online monitoring data of diesel vehicles is supplemented with a timestamp and a vehicle identification code; A data processing module, used for cleaning and filtering the diesel vehicle online monitoring data based on data processing rules to ensure the accuracy of the data; A calculation module is used to calculate the nitrogen oxide mass flow rate and emission factor of the diesel vehicle according to the processed data; the calculation process of the emission factor includes: judging whether the vehicle travel meets the second-by-second sampling characteristics according to the processed data; if so, summing up the nitrogen oxide mass flow rate and the engine fuel flow rate respectively, and taking the ratio of the two as the emission factor for the day; otherwise, calculating the emission factor second by second based on the ratio of the nitrogen oxide mass flow rate and the engine fuel flow rate, and taking the median of the data sequence as the emission factor for the day; Identification module for determining high-emitting diesel vehicles based on NOx mass flow and emission factors.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the high-emission diesel vehicle identification method according to any one of claims 1 to 8 is implemented.

Images